Overview

Dataset statistics

Number of variables19
Number of observations782
Missing cells1246
Missing cells (%)8.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory420.3 KiB
Average record size in memory550.3 B

Variable types

Text2
Numeric10
DateTime1
Categorical6

Alerts

alert is highly overall correlated with sigHigh correlation
cdi is highly overall correlated with sigHigh correlation
continent is highly overall correlated with country and 1 other fieldsHigh correlation
country is highly overall correlated with continent and 2 other fieldsHigh correlation
dmin is highly overall correlated with nstHigh correlation
gap is highly overall correlated with netHigh correlation
latitude is highly overall correlated with countryHigh correlation
longitude is highly overall correlated with continent and 1 other fieldsHigh correlation
magType is highly overall correlated with net and 1 other fieldsHigh correlation
magnitude is highly overall correlated with sigHigh correlation
net is highly overall correlated with gap and 1 other fieldsHigh correlation
nst is highly overall correlated with dmin and 1 other fieldsHigh correlation
sig is highly overall correlated with alert and 2 other fieldsHigh correlation
tsunami is highly overall correlated with magType and 1 other fieldsHigh correlation
net is highly imbalanced (88.7%)Imbalance
magType is highly imbalanced (52.7%)Imbalance
alert has 367 (46.9%) missing valuesMissing
continent has 576 (73.7%) missing valuesMissing
country has 298 (38.1%) missing valuesMissing
cdi has 212 (27.1%) zerosZeros
nst has 365 (46.7%) zerosZeros
dmin has 405 (51.8%) zerosZeros
gap has 70 (9.0%) zerosZeros

Reproduction

Analysis started2026-01-09 11:38:00.376139
Analysis finished2026-01-09 11:38:22.371127
Duration21.99 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

title
Text

Distinct768
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size70.4 KiB
2026-01-09T12:38:22.913132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length64
Median length52
Mean length38.230179
Min length8

Characters and Unicode

Total characters29896
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique756 ?
Unique (%)96.7%

Sample

1st rowM 7.0 - 18 km SW of Malango, Solomon Islands
2nd rowM 6.9 - 204 km SW of Bengkulu, Indonesia
3rd rowM 7.0 -
4th rowM 7.3 - 205 km ESE of Neiafu, Tonga
5th rowM 6.6 -
ValueCountFrequency (%)
784
 
11.0%
m782
 
11.0%
of700
 
9.8%
km653
 
9.2%
6.5131
 
1.8%
6.6115
 
1.6%
indonesia106
 
1.5%
new105
 
1.5%
6.798
 
1.4%
islands96
 
1.3%
Other values (825)3559
49.9%
2026-01-09T12:38:23.676283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6352
21.2%
a1972
 
6.6%
o1523
 
5.1%
n1186
 
4.0%
i1017
 
3.4%
e963
 
3.2%
M889
 
3.0%
m867
 
2.9%
k866
 
2.9%
-819
 
2.7%
Other values (71)13442
45.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)29896
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6352
21.2%
a1972
 
6.6%
o1523
 
5.1%
n1186
 
4.0%
i1017
 
3.4%
e963
 
3.2%
M889
 
3.0%
m867
 
2.9%
k866
 
2.9%
-819
 
2.7%
Other values (71)13442
45.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)29896
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6352
21.2%
a1972
 
6.6%
o1523
 
5.1%
n1186
 
4.0%
i1017
 
3.4%
e963
 
3.2%
M889
 
3.0%
m867
 
2.9%
k866
 
2.9%
-819
 
2.7%
Other values (71)13442
45.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)29896
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6352
21.2%
a1972
 
6.6%
o1523
 
5.1%
n1186
 
4.0%
i1017
 
3.4%
e963
 
3.2%
M889
 
3.0%
m867
 
2.9%
k866
 
2.9%
-819
 
2.7%
Other values (71)13442
45.0%

magnitude
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9411253
Minimum6.5
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2026-01-09T12:38:23.858372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.5
5-th percentile6.5
Q16.6
median6.8
Q37.1
95-th percentile7.8
Maximum9.1
Range2.6
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.445514
Coefficient of variation (CV)0.064184694
Kurtosis2.2263913
Mean6.9411253
Median Absolute Deviation (MAD)0.2
Skewness1.44444
Sum5427.96
Variance0.19848273
MonotonicityNot monotonic
2026-01-09T12:38:24.040349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
6.5131
16.8%
6.6115
14.7%
6.798
12.5%
6.878
10.0%
6.977
9.8%
749
 
6.3%
7.143
 
5.5%
7.331
 
4.0%
7.230
 
3.8%
7.622
 
2.8%
Other values (14)108
13.8%
ValueCountFrequency (%)
6.5131
16.8%
6.6115
14.7%
6.798
12.5%
6.878
10.0%
6.977
9.8%
749
 
6.3%
7.143
 
5.5%
7.230
 
3.8%
7.331
 
4.0%
7.418
 
2.3%
ValueCountFrequency (%)
9.12
 
0.3%
8.81
 
0.1%
8.62
 
0.3%
8.42
 
0.3%
8.33
 
0.4%
8.26
0.8%
8.161
 
0.1%
8.16
0.8%
85
0.6%
7.99
1.2%
Distinct773
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
Minimum2001-01-01 06:57:00
Maximum2022-12-11 07:09:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-09T12:38:24.235374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T12:38:24.435560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cdi
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3337596
Minimum0
Maximum9
Zeros212
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2026-01-09T12:38:24.648629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.169939
Coefficient of variation (CV)0.73145243
Kurtosis-1.3577532
Mean4.3337596
Median Absolute Deviation (MAD)3
Skewness-0.19731027
Sum3389
Variance10.048513
MonotonicityNot monotonic
2026-01-09T12:38:24.767668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0212
27.1%
5107
13.7%
797
12.4%
886
11.0%
677
 
9.8%
966
 
8.4%
462
 
7.9%
347
 
6.0%
114
 
1.8%
214
 
1.8%
ValueCountFrequency (%)
0212
27.1%
114
 
1.8%
214
 
1.8%
347
 
6.0%
462
 
7.9%
5107
13.7%
677
 
9.8%
797
12.4%
886
11.0%
966
 
8.4%
ValueCountFrequency (%)
966
 
8.4%
886
11.0%
797
12.4%
677
 
9.8%
5107
13.7%
462
 
7.9%
347
 
6.0%
214
 
1.8%
114
 
1.8%
0212
27.1%

mmi
Real number (ℝ)

Distinct9
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9641944
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2026-01-09T12:38:24.930124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median6
Q37
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.462724
Coefficient of variation (CV)0.24525089
Kurtosis-0.2245919
Mean5.9641944
Median Absolute Deviation (MAD)1
Skewness-0.25040262
Sum4664
Variance2.1395614
MonotonicityNot monotonic
2026-01-09T12:38:25.066116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7209
26.7%
6203
26.0%
5142
18.2%
487
11.1%
868
 
8.7%
340
 
5.1%
928
 
3.6%
24
 
0.5%
11
 
0.1%
ValueCountFrequency (%)
11
 
0.1%
24
 
0.5%
340
 
5.1%
487
11.1%
5142
18.2%
6203
26.0%
7209
26.7%
868
 
8.7%
928
 
3.6%
ValueCountFrequency (%)
928
 
3.6%
868
 
8.7%
7209
26.7%
6203
26.0%
5142
18.2%
487
11.1%
340
 
5.1%
24
 
0.5%
11
 
0.1%

alert
Categorical

High correlation  Missing 

Distinct4
Distinct (%)1.0%
Missing367
Missing (%)46.9%
Memory size42.1 KiB
green
325 
yellow
56 
orange
 
22
red
 
12

Length

Max length6
Median length5
Mean length5.1301205
Min length3

Characters and Unicode

Total characters2129
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgreen
2nd rowgreen
3rd rowgreen
4th rowgreen
5th rowgreen

Common Values

ValueCountFrequency (%)
green325
41.6%
yellow56
 
7.2%
orange22
 
2.8%
red12
 
1.5%
(Missing)367
46.9%

Length

2026-01-09T12:38:25.222579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T12:38:25.349126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
green325
78.3%
yellow56
 
13.5%
orange22
 
5.3%
red12
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e740
34.8%
r359
16.9%
g347
16.3%
n347
16.3%
l112
 
5.3%
o78
 
3.7%
y56
 
2.6%
w56
 
2.6%
a22
 
1.0%
d12
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)2129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e740
34.8%
r359
16.9%
g347
16.3%
n347
16.3%
l112
 
5.3%
o78
 
3.7%
y56
 
2.6%
w56
 
2.6%
a22
 
1.0%
d12
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e740
34.8%
r359
16.9%
g347
16.3%
n347
16.3%
l112
 
5.3%
o78
 
3.7%
y56
 
2.6%
w56
 
2.6%
a22
 
1.0%
d12
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e740
34.8%
r359
16.9%
g347
16.3%
n347
16.3%
l112
 
5.3%
o78
 
3.7%
y56
 
2.6%
w56
 
2.6%
a22
 
1.0%
d12
 
0.6%

tsunami
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size38.3 KiB
0
478 
1
304 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters782
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0478
61.1%
1304
38.9%

Length

2026-01-09T12:38:25.483166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T12:38:25.579665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0478
61.1%
1304
38.9%

Most occurring characters

ValueCountFrequency (%)
0478
61.1%
1304
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)782
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0478
61.1%
1304
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)782
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0478
61.1%
1304
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)782
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0478
61.1%
1304
38.9%

sig
Real number (ℝ)

High correlation 

Distinct339
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean870.1087
Minimum650
Maximum2910
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2026-01-09T12:38:25.711291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum650
5-th percentile650
Q1691
median754
Q3909.75
95-th percentile1550.7
Maximum2910
Range2260
Interquartile range (IQR)218.75

Descriptive statistics

Standard deviation322.46537
Coefficient of variation (CV)0.37060354
Kurtosis12.000754
Mean870.1087
Median Absolute Deviation (MAD)84
Skewness3.0836291
Sum680425
Variance103983.91
MonotonicityNot monotonic
2026-01-09T12:38:25.887693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65050
 
6.4%
67041
 
5.2%
69136
 
4.6%
71125
 
3.2%
77618
 
2.3%
73215
 
1.9%
82012
 
1.5%
65111
 
1.4%
8429
 
1.2%
7549
 
1.2%
Other values (329)556
71.1%
ValueCountFrequency (%)
65050
6.4%
65111
 
1.4%
6526
 
0.8%
6535
 
0.6%
6543
 
0.4%
6553
 
0.4%
6562
 
0.3%
6574
 
0.5%
6591
 
0.1%
6611
 
0.1%
ValueCountFrequency (%)
29102
0.3%
28401
0.1%
28201
0.1%
27901
0.1%
25041
0.1%
23971
0.1%
23311
0.1%
21841
0.1%
20831
0.1%
20741
0.1%

net
Categorical

High correlation  Imbalance 

Distinct11
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size39.1 KiB
us
747 
ak
 
11
official
 
8
nc
 
3
duputel
 
3
Other values (6)
 
10

Length

Max length8
Median length2
Mean length2.0805627
Min length2

Characters and Unicode

Total characters1627
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st rowus
2nd rowus
3rd rowus
4th rowus
5th rowus

Common Values

ValueCountFrequency (%)
us747
95.5%
ak11
 
1.4%
official8
 
1.0%
nc3
 
0.4%
duputel3
 
0.4%
pt2
 
0.3%
at2
 
0.3%
ci2
 
0.3%
hv2
 
0.3%
nn1
 
0.1%

Length

2026-01-09T12:38:26.046777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us747
95.5%
ak11
 
1.4%
official8
 
1.0%
nc3
 
0.4%
duputel3
 
0.4%
pt2
 
0.3%
at2
 
0.3%
ci2
 
0.3%
hv2
 
0.3%
nn1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
u754
46.3%
s747
45.9%
a21
 
1.3%
i18
 
1.1%
f16
 
1.0%
c13
 
0.8%
k11
 
0.7%
l11
 
0.7%
o8
 
0.5%
t7
 
0.4%
Other values (7)21
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1627
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u754
46.3%
s747
45.9%
a21
 
1.3%
i18
 
1.1%
f16
 
1.0%
c13
 
0.8%
k11
 
0.7%
l11
 
0.7%
o8
 
0.5%
t7
 
0.4%
Other values (7)21
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1627
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u754
46.3%
s747
45.9%
a21
 
1.3%
i18
 
1.1%
f16
 
1.0%
c13
 
0.8%
k11
 
0.7%
l11
 
0.7%
o8
 
0.5%
t7
 
0.4%
Other values (7)21
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1627
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u754
46.3%
s747
45.9%
a21
 
1.3%
i18
 
1.1%
f16
 
1.0%
c13
 
0.8%
k11
 
0.7%
l11
 
0.7%
o8
 
0.5%
t7
 
0.4%
Other values (7)21
 
1.3%

nst
Real number (ℝ)

High correlation  Zeros 

Distinct312
Distinct (%)39.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230.25064
Minimum0
Maximum934
Zeros365
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2026-01-09T12:38:26.249223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median140
Q3445
95-th percentile663.95
Maximum934
Range934
Interquartile range (IQR)445

Descriptive statistics

Standard deviation250.18818
Coefficient of variation (CV)1.0865906
Kurtosis-1.0927934
Mean230.25064
Median Absolute Deviation (MAD)140
Skewness0.53330716
Sum180056
Variance62594.124
MonotonicityNot monotonic
2026-01-09T12:38:26.446886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0365
46.7%
2824
 
0.5%
3984
 
0.5%
5184
 
0.5%
3853
 
0.4%
4463
 
0.4%
5963
 
0.4%
4093
 
0.4%
6983
 
0.4%
3973
 
0.4%
Other values (302)387
49.5%
ValueCountFrequency (%)
0365
46.7%
101
 
0.1%
201
 
0.1%
231
 
0.1%
271
 
0.1%
431
 
0.1%
501
 
0.1%
511
 
0.1%
631
 
0.1%
641
 
0.1%
ValueCountFrequency (%)
9341
0.1%
9291
0.1%
9181
0.1%
8621
0.1%
8071
0.1%
7981
0.1%
7822
0.3%
7741
0.1%
7701
0.1%
7691
0.1%

dmin
Real number (ℝ)

High correlation  Zeros 

Distinct369
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3257571
Minimum0
Maximum17.654
Zeros405
Zeros (%)51.8%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2026-01-09T12:38:26.622580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.863
95-th percentile5.7895
Maximum17.654
Range17.654
Interquartile range (IQR)1.863

Descriptive statistics

Standard deviation2.218805
Coefficient of variation (CV)1.6736135
Kurtosis9.283367
Mean1.3257571
Median Absolute Deviation (MAD)0
Skewness2.6045797
Sum1036.742
Variance4.9230954
MonotonicityNot monotonic
2026-01-09T12:38:26.838378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0405
51.8%
3.1442
 
0.3%
0.8282
 
0.3%
0.2892
 
0.3%
2.7052
 
0.3%
1.5052
 
0.3%
1.7782
 
0.3%
2.0452
 
0.3%
1.4872
 
0.3%
0.7782
 
0.3%
Other values (359)359
45.9%
ValueCountFrequency (%)
0405
51.8%
0.046161
 
0.1%
0.046851
 
0.1%
0.071
 
0.1%
0.111
 
0.1%
0.1331
 
0.1%
0.1351
 
0.1%
0.1421
 
0.1%
0.1511
 
0.1%
0.1731
 
0.1%
ValueCountFrequency (%)
17.6541
0.1%
15.3941
0.1%
12.8961
0.1%
11.7641
0.1%
11.4111
0.1%
11.2551
0.1%
10.6691
0.1%
10.4051
0.1%
9.7991
0.1%
8.8651
0.1%

gap
Real number (ℝ)

High correlation  Zeros 

Distinct256
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.03899
Minimum0
Maximum239
Zeros70
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2026-01-09T12:38:27.086353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.625
median20
Q330
95-th percentile55.9895
Maximum239
Range239
Interquartile range (IQR)15.375

Descriptive statistics

Standard deviation24.225067
Coefficient of variation (CV)0.96749378
Kurtosis32.027722
Mean25.03899
Median Absolute Deviation (MAD)7
Skewness4.6686068
Sum19580.49
Variance586.85386
MonotonicityNot monotonic
2026-01-09T12:38:27.281135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
070
 
9.0%
1823
 
2.9%
1622
 
2.8%
2222
 
2.8%
1220
 
2.6%
1920
 
2.6%
1719
 
2.4%
1518
 
2.3%
1418
 
2.3%
1116
 
2.0%
Other values (246)534
68.3%
ValueCountFrequency (%)
070
9.0%
81
 
0.1%
8.71
 
0.1%
97
 
0.9%
9.52
 
0.3%
108
 
1.0%
10.13
 
0.4%
10.21
 
0.1%
10.61
 
0.1%
10.81
 
0.1%
ValueCountFrequency (%)
2391
0.1%
2291
0.1%
2201
0.1%
2101
0.1%
208.81
0.1%
205.21
0.1%
1261
0.1%
1241
0.1%
1231
0.1%
1191
0.1%

magType
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size39.8 KiB
mww
468 
mwc
217 
mwb
70 
mw
 
16
Mi
 
4
Other values (4)
 
7

Length

Max length3
Median length3
Mean length2.9654731
Min length2

Characters and Unicode

Total characters2319
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowmww
2nd rowmww
3rd rowmww
4th rowmww
5th rowmww

Common Values

ValueCountFrequency (%)
mww468
59.8%
mwc217
27.7%
mwb70
 
9.0%
mw16
 
2.0%
Mi4
 
0.5%
mb2
 
0.3%
ms2
 
0.3%
md2
 
0.3%
ml1
 
0.1%

Length

2026-01-09T12:38:27.523381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T12:38:27.856318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mww468
59.8%
mwc217
27.7%
mwb70
 
9.0%
mw16
 
2.0%
mi4
 
0.5%
mb2
 
0.3%
ms2
 
0.3%
md2
 
0.3%
ml1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
w1239
53.4%
m778
33.5%
c217
 
9.4%
b72
 
3.1%
M4
 
0.2%
i4
 
0.2%
s2
 
0.1%
d2
 
0.1%
l1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
w1239
53.4%
m778
33.5%
c217
 
9.4%
b72
 
3.1%
M4
 
0.2%
i4
 
0.2%
s2
 
0.1%
d2
 
0.1%
l1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
w1239
53.4%
m778
33.5%
c217
 
9.4%
b72
 
3.1%
M4
 
0.2%
i4
 
0.2%
s2
 
0.1%
d2
 
0.1%
l1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
w1239
53.4%
m778
33.5%
c217
 
9.4%
b72
 
3.1%
M4
 
0.2%
i4
 
0.2%
s2
 
0.1%
d2
 
0.1%
l1
 
< 0.1%

depth
Real number (ℝ)

Distinct303
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.883199
Minimum2.7
Maximum670.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2026-01-09T12:38:28.043034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile10
Q114
median26.295
Q349.75
95-th percentile526.9
Maximum670.81
Range668.11
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation137.27708
Coefficient of variation (CV)1.8090576
Kurtosis8.3844796
Mean75.883199
Median Absolute Deviation (MAD)14.295
Skewness3.0248691
Sum59340.662
Variance18844.996
MonotonicityNot monotonic
2026-01-09T12:38:28.280910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1092
 
11.8%
3525
 
3.2%
2025
 
3.2%
3323
 
2.9%
1221
 
2.7%
2419
 
2.4%
1117
 
2.2%
2115
 
1.9%
2213
 
1.7%
1813
 
1.7%
Other values (293)519
66.4%
ValueCountFrequency (%)
2.71
 
0.1%
4.22
 
0.3%
53
0.4%
5.811
 
0.1%
61
 
0.1%
6.432
 
0.3%
6.81
 
0.1%
72
 
0.3%
7.81
 
0.1%
87
0.9%
ValueCountFrequency (%)
670.811
0.1%
6641
0.1%
6601
0.1%
630.3791
0.1%
6301
0.1%
627.171
0.1%
624.4641
0.1%
6242
0.3%
622.731
0.1%
620.561
0.1%

latitude
Real number (ℝ)

High correlation 

Distinct778
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5380999
Minimum-61.8484
Maximum71.6312
Zeros0
Zeros (%)0.0%
Negative424
Negative (%)54.2%
Memory size6.2 KiB
2026-01-09T12:38:28.488515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-61.8484
5-th percentile-37.58413
Q1-14.5956
median-2.5725
Q324.6545
95-th percentile52.390155
Maximum71.6312
Range133.4796
Interquartile range (IQR)39.2501

Descriptive statistics

Standard deviation27.303429
Coefficient of variation (CV)7.7169753
Kurtosis-0.47674034
Mean3.5380999
Median Absolute Deviation (MAD)17.01635
Skewness0.20085297
Sum2766.7941
Variance745.47724
MonotonicityNot monotonic
2026-01-09T12:38:28.683747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.2762
 
0.3%
52.5022
 
0.3%
52.482
 
0.3%
1.2712
 
0.3%
-13.5711
 
0.1%
-37.7731
 
0.1%
-36.1221
 
0.1%
25.931
 
0.1%
18.4431
 
0.1%
40.6521
 
0.1%
Other values (768)768
98.2%
ValueCountFrequency (%)
-61.84841
0.1%
-60.8571
0.1%
-60.5321
0.1%
-60.30261
0.1%
-60.27381
0.1%
-60.26271
0.1%
-60.21521
0.1%
-60.10231
0.1%
-58.62621
0.1%
-58.54461
0.1%
ValueCountFrequency (%)
71.63121
0.1%
67.6311
0.1%
63.51441
0.1%
63.51411
0.1%
61.34641
0.1%
60.9491
0.1%
60.7721
0.1%
60.4911
0.1%
59.62041
0.1%
58.7751
0.1%

longitude
Real number (ℝ)

High correlation 

Distinct777
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.609199
Minimum-179.968
Maximum179.662
Zeros0
Zeros (%)0.0%
Negative261
Negative (%)33.4%
Memory size6.2 KiB
2026-01-09T12:38:28.865803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-179.968
5-th percentile-174.70275
Q1-71.66805
median109.426
Q3148.941
95-th percentile168.8589
Maximum179.662
Range359.63
Interquartile range (IQR)220.60905

Descriptive statistics

Standard deviation117.89889
Coefficient of variation (CV)2.2410317
Kurtosis-1.0883829
Mean52.609199
Median Absolute Deviation (MAD)52.729
Skewness-0.70298243
Sum41140.394
Variance13900.147
MonotonicityNot monotonic
2026-01-09T12:38:29.052109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
168.1432
 
0.3%
168.8922
 
0.3%
-168.082
 
0.3%
-167.7362
 
0.3%
107.4192
 
0.3%
-75.0481
 
0.1%
-72.8981
 
0.1%
128.4251
 
0.1%
-72.5711
 
0.1%
-124.6921
 
0.1%
Other values (767)767
98.1%
ValueCountFrequency (%)
-179.9681
0.1%
-179.5111
0.1%
-179.3731
0.1%
-178.9591
0.1%
-178.9271
0.1%
-178.8041
0.1%
-178.61
0.1%
-178.571
0.1%
-178.41
0.1%
-178.3461
0.1%
ValueCountFrequency (%)
179.6621
0.1%
179.5441
0.1%
179.351
0.1%
179.1461
0.1%
178.7351
0.1%
178.3811
0.1%
178.3631
0.1%
178.3311
0.1%
178.2911
0.1%
178.2781
0.1%
Distinct413
Distinct (%)53.2%
Missing5
Missing (%)0.6%
Memory size54.3 KiB
2026-01-09T12:38:29.409661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length42
Median length33
Mean length19.148005
Min length1

Characters and Unicode

Total characters14878
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique274 ?
Unique (%)35.3%

Sample

1st rowMalango, Solomon Islands
2nd rowBengkulu, Indonesia
3rd rowNeiafu, Tonga
4th rowthe Fiji Islands
5th rowthe Fiji Islands
ValueCountFrequency (%)
indonesia105
 
5.1%
new105
 
5.1%
islands96
 
4.7%
papua69
 
3.4%
guinea67
 
3.3%
japan64
 
3.1%
chile48
 
2.3%
vanuatu43
 
2.1%
region41
 
2.0%
solomon39
 
1.9%
Other values (543)1375
67.0%
2026-01-09T12:38:29.916570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a1950
 
13.1%
1275
 
8.6%
n1181
 
7.9%
i1012
 
6.8%
e941
 
6.3%
o802
 
5.4%
,715
 
4.8%
u610
 
4.1%
l602
 
4.0%
s589
 
4.0%
Other values (65)5201
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)14878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1950
 
13.1%
1275
 
8.6%
n1181
 
7.9%
i1012
 
6.8%
e941
 
6.3%
o802
 
5.4%
,715
 
4.8%
u610
 
4.1%
l602
 
4.0%
s589
 
4.0%
Other values (65)5201
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1950
 
13.1%
1275
 
8.6%
n1181
 
7.9%
i1012
 
6.8%
e941
 
6.3%
o802
 
5.4%
,715
 
4.8%
u610
 
4.1%
l602
 
4.0%
s589
 
4.0%
Other values (65)5201
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1950
 
13.1%
1275
 
8.6%
n1181
 
7.9%
i1012
 
6.8%
e941
 
6.3%
o802
 
5.4%
,715
 
4.8%
u610
 
4.1%
l602
 
4.0%
s589
 
4.0%
Other values (65)5201
35.0%

continent
Categorical

High correlation  Missing 

Distinct6
Distinct (%)2.9%
Missing576
Missing (%)73.7%
Memory size43.1 KiB
Asia
100 
South America
55 
North America
34 
Europe
 
10
Oceania
 
4

Length

Max length13
Median length7
Mean length8.0728155
Min length4

Characters and Unicode

Total characters1663
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOceania
2nd rowOceania
3rd rowNorth America
4th rowNorth America
5th rowAsia

Common Values

ValueCountFrequency (%)
Asia100
 
12.8%
South America55
 
7.0%
North America34
 
4.3%
Europe10
 
1.3%
Oceania4
 
0.5%
Africa3
 
0.4%
(Missing)576
73.7%

Length

2026-01-09T12:38:30.052907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-09T12:38:30.185871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
asia100
33.9%
america89
30.2%
south55
18.6%
north34
 
11.5%
europe10
 
3.4%
oceania4
 
1.4%
africa3
 
1.0%

Most occurring characters

ValueCountFrequency (%)
a200
12.0%
i196
11.8%
A192
11.5%
r136
 
8.2%
e103
 
6.2%
s100
 
6.0%
o99
 
6.0%
c96
 
5.8%
89
 
5.4%
m89
 
5.4%
Other values (10)363
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a200
12.0%
i196
11.8%
A192
11.5%
r136
 
8.2%
e103
 
6.2%
s100
 
6.0%
o99
 
6.0%
c96
 
5.8%
89
 
5.4%
m89
 
5.4%
Other values (10)363
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a200
12.0%
i196
11.8%
A192
11.5%
r136
 
8.2%
e103
 
6.2%
s100
 
6.0%
o99
 
6.0%
c96
 
5.8%
89
 
5.4%
m89
 
5.4%
Other values (10)363
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a200
12.0%
i196
11.8%
A192
11.5%
r136
 
8.2%
e103
 
6.2%
s100
 
6.0%
o99
 
6.0%
c96
 
5.8%
89
 
5.4%
m89
 
5.4%
Other values (10)363
21.8%

country
Categorical

High correlation  Missing 

Distinct49
Distinct (%)10.1%
Missing298
Missing (%)38.1%
Memory size44.5 KiB
Indonesia
110 
Papua New Guinea
56 
Chile
34 
Vanuatu
27 
Solomon Islands
 
22
Other values (44)
235 

Length

Max length58
Median length24
Mean length10.297521
Min length4

Characters and Unicode

Total characters4984
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)3.3%

Sample

1st rowSolomon Islands
2nd rowFiji
3rd rowPanama
4th rowMexico
5th rowMexico

Common Values

ValueCountFrequency (%)
Indonesia110
 
14.1%
Papua New Guinea56
 
7.2%
Chile34
 
4.3%
Vanuatu27
 
3.5%
Solomon Islands22
 
2.8%
Japan21
 
2.7%
Peru20
 
2.6%
Mexico20
 
2.6%
Philippines17
 
2.2%
United States of America17
 
2.2%
Other values (39)140
17.9%
(Missing)298
38.1%

Length

2026-01-09T12:38:30.434312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
indonesia110
 
14.4%
new65
 
8.5%
papua56
 
7.3%
guinea56
 
7.3%
chile34
 
4.5%
of34
 
4.5%
vanuatu27
 
3.5%
islands23
 
3.0%
solomon22
 
2.9%
united22
 
2.9%
Other values (57)315
41.2%

Most occurring characters

ValueCountFrequency (%)
a625
12.5%
n514
 
10.3%
e477
 
9.6%
i439
 
8.8%
o281
 
5.6%
280
 
5.6%
s248
 
5.0%
u230
 
4.6%
d194
 
3.9%
l155
 
3.1%
Other values (39)1541
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)4984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a625
12.5%
n514
 
10.3%
e477
 
9.6%
i439
 
8.8%
o281
 
5.6%
280
 
5.6%
s248
 
5.0%
u230
 
4.6%
d194
 
3.9%
l155
 
3.1%
Other values (39)1541
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a625
12.5%
n514
 
10.3%
e477
 
9.6%
i439
 
8.8%
o281
 
5.6%
280
 
5.6%
s248
 
5.0%
u230
 
4.6%
d194
 
3.9%
l155
 
3.1%
Other values (39)1541
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a625
12.5%
n514
 
10.3%
e477
 
9.6%
i439
 
8.8%
o281
 
5.6%
280
 
5.6%
s248
 
5.0%
u230
 
4.6%
d194
 
3.9%
l155
 
3.1%
Other values (39)1541
30.9%

Interactions

2026-01-09T12:38:18.780410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-09T12:38:02.880407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-09T12:38:18.584864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-09T12:38:30.580640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
alertcdicontinentcountrydepthdmingaplatitudelongitudemagTypemagnitudemminetnstsigtsunami
alert1.0000.3120.1310.4140.0000.0000.1160.3000.2420.0000.1230.4240.1150.0000.6620.146
cdi0.3121.0000.1040.264-0.0400.1750.1250.138-0.1960.1370.2340.3530.019-0.2000.5730.277
continent0.1310.1041.0000.9150.1710.0000.1140.4820.6750.2210.0000.0890.1960.1460.1630.303
country0.4140.2640.9151.0000.3110.3900.0000.7900.7690.1330.0000.3350.0000.1970.3360.385
depth0.000-0.0400.1710.3111.0000.049-0.185-0.0760.0270.0000.124-0.2540.0000.0080.0390.054
dmin0.0000.1750.0000.3900.0491.0000.072-0.193-0.0780.133-0.091-0.2590.000-0.813-0.0630.404
gap0.1160.1250.1140.000-0.1850.0721.000-0.015-0.2840.344-0.137-0.0040.514-0.041-0.0080.047
latitude0.3000.1380.4820.790-0.076-0.193-0.0151.000-0.1090.173-0.0150.1520.1950.1590.1720.323
longitude0.242-0.1960.6750.7690.027-0.078-0.284-0.1091.0000.1760.024-0.0890.2130.103-0.1790.419
magType0.0000.1370.2210.1330.0000.1330.3440.1730.1761.0000.0120.0990.6540.2750.1350.610
magnitude0.1230.2340.0000.0000.124-0.091-0.137-0.0150.0240.0121.0000.2590.3080.1030.7690.033
mmi0.4240.3530.0890.335-0.254-0.259-0.0040.152-0.0890.0990.2591.0000.1200.1450.4360.163
net0.1150.0190.1960.0000.0000.0000.5140.1950.2130.6540.3080.1201.0000.0000.2030.095
nst0.000-0.2000.1460.1970.008-0.813-0.0410.1590.1030.2750.1030.1450.0001.0000.0380.639
sig0.6620.5730.1630.3360.039-0.063-0.0080.172-0.1790.1350.7690.4360.2030.0381.0000.081
tsunami0.1460.2770.3030.3850.0540.4040.0470.3230.4190.6100.0330.1630.0950.6390.0811.000

Missing values

2026-01-09T12:38:21.659915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-09T12:38:22.019726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-09T12:38:22.255099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

titlemagnitudedate_timecdimmialerttsunamisignetnstdmingapmagTypedepthlatitudelongitudelocationcontinentcountry
0M 7.0 - 18 km SW of Malango, Solomon Islands7.022-11-2022 02:0387green1768us1170.50917.0mww14.000-9.7963159.5960Malango, Solomon IslandsOceaniaSolomon Islands
1M 6.9 - 204 km SW of Bengkulu, Indonesia6.918-11-2022 13:3744green0735us992.22934.0mww25.000-4.9559100.7380Bengkulu, IndonesiaNaNNaN
2M 7.0 -7.012-11-2022 07:0933green1755us1473.12518.0mww579.000-20.0508-178.3460NaNOceaniaFiji
3M 7.3 - 205 km ESE of Neiafu, Tonga7.311-11-2022 10:4855green1833us1491.86521.0mww37.000-19.2918-172.1290Neiafu, TongaNaNNaN
4M 6.6 -6.609-11-2022 10:1402green1670us1314.99827.0mww624.464-25.5948178.2780NaNNaNNaN
5M 7.0 - south of the Fiji Islands7.009-11-2022 09:5143green1755us1424.57826.0mwb660.000-26.0442178.3810the Fiji IslandsNaNNaN
6M 6.8 - south of the Fiji Islands6.809-11-2022 09:3813green1711us1364.67822.0mww630.379-25.9678178.3630the Fiji IslandsNaNNaN
7M 6.7 - 60 km SSW of Boca Chica, Panama6.720-10-2022 11:5776green1797us1451.15137.0mww20.0007.6712-82.3396Boca Chica, PanamaNaNPanama
8M 6.8 - 55 km SSW of Aguililla, Mexico6.822-09-2022 06:1687yellow11179us1752.13792.0mww20.00018.3300-102.9130Aguililla, MexicoNorth AmericaMexico
9M 7.6 - 35 km SSW of Aguililla, Mexico7.619-09-2022 18:0598yellow11799us2711.15369.0mww26.94318.3667-103.2520Aguililla, MexicoNorth AmericaMexico
titlemagnitudedate_timecdimmialerttsunamisignetnstdmingapmagTypedepthlatitudelongitudelocationcontinentcountry
772M 7.1 - 137 km WNW of Ternate, Indonesia7.124-02-2001 07:2307NaN0776us4260.00.0mwc35.01.2710126.249Ternate, IndonesiaNaNIndonesia
773M 7.4 - 102 km SSE of Bengkulu, Indonesia7.413-02-2001 19:2806NaN0842us2210.00.0mwc36.0-4.6800102.562Bengkulu, IndonesiaNaNIndonesia
774M 6.6 - 5 km S of Cojutepeque, El Salvador6.613-02-2001 14:2208NaN0670us2730.00.0mwc10.013.6710-88.938Cojutepeque, El SalvadorNaNEl Salvador
775M 7.7 - 17 km NW of Bhach?u, India7.726-01-2001 03:1609NaN0912us4720.00.0mwc16.023.419070.232Bhach?u, IndiaAsiaIndia
776M 6.9 - 59 km WSW of Bengkulu, Indonesia6.916-01-2001 13:2506NaN0732us1170.00.0mwb28.0-4.0220101.776Bengkulu, IndonesiaNaNIndonesia
777M 7.7 - 28 km SSW of Puerto El Triunfo, El Salvador7.713-01-2001 17:3308NaN0912us4270.00.0mwc60.013.0490-88.660Puerto El Triunfo, El SalvadorNaNNaN
778M 6.9 - 47 km S of Old Harbor, Alaska6.910-01-2001 16:0257NaN0745ak00.00.0mw36.456.7744-153.281Old Harbor, AlaskaNorth AmericaNaN
779M 7.1 - 16 km NE of Port-Olry, Vanuatu7.109-01-2001 16:4907NaN0776us3720.00.0mwb103.0-14.9280167.170Port-Olry, VanuatuNaNVanuatu
780M 6.8 - Mindanao, Philippines6.801-01-2001 08:5405NaN0711us640.00.0mwc33.06.6310126.899Mindanao, PhilippinesNaNNaN
781M 7.5 - 21 km SE of Lukatan, Philippines7.501-01-2001 06:5707NaN0865us3240.00.0mwc33.06.8980126.579Lukatan, PhilippinesNaNPhilippines